In mobile radio systems, the signals propagate in the form of multiple paths whose influence on the signal can be described in the form of a linear time-variant transformation. Signal distortions such as this make correct detection of the data transmitted between the base station and the mobile radio impossible or extremely difficult. For this reason, for example in the case of data transmission which is based on the UMTS (Universal Mobile Telecommunications System) Standard, the channel distortion is estimated with the aid of a pilot signal (Common Pilot Channel; CPICH). The pilot signal is a signal which is transmitted from the base station and by means of which the same pilot symbol or a continuously recurring pattern of two different pilot symbols is transmitted continuously.
In one simple channel model, the symbols rk which are received by the mobile radio can be mathematically described as follows:rk=sk·ck+nk  (1)
In this case, sk represents the symbols which are transmitted from the base station, ck a channel parameter and nk a noise element. The channel parameter ck describes the rotation stretching of the symbols sk in the transmission channel. The integer index k indicates the time sequence of the symbols. All the variables in the equation (1) represent complex numbers.
The equation (1) can, of course, also be applied to the transmitted pilot symbols. If the noise element nk is ignored, then the channel parameter ck can be determined by multiplying the received pilot symbols rk by the complex-conjugate known pilot symbols sk. The influence of the transmission channel on the transmitted symbols after their reception in the mobile radio can be eliminated with the aid of the channel parameter ck obtained in this way, using the equation (1). However, physical effects in the radio frequency receiver mean that the received signals are noisy, so that the channel parameter ck can be estimated only with finite accuracy.
In order to improve the accuracy of the channel estimate, it is possible to use optimum adaptive algorithms which take account of statistical parameters relating to the transmission channel, such as the correlation between adjacent channel values and noise.
Known channel estimators are essentially based on two principles.
In a first type of channel estimator, no assumptions or ad-hoc assumptions are made about the statistical characteristics of the transmission channel. The complexity of these channel estimators is low since, for example, they can be designed using filters with fixed, complexity-optimized coefficients. However, if the actual channel characteristics differ to a major extent from the assumptions that have been made, the use of fixed estimation coefficients leads to only unsatisfactory results.
A second type of channel estimator is used when the estimation process is subject to stringent quality requirements.
These channel estimators are able to set their characteristics adaptively, that is to say the coefficients of these channel estimators are matched to the channel conditions actually present at the respective time. Channel estimators of this type include, for example, adaptive Wiener channel estimators. The method of operation of adaptive channel estimators admittedly leads to good results, but the degree of complexity for adaptive optimization of the coefficients is very high.